Theoretically evaluate the consequences of a hypothetical proof that P equals NP (or P does not equal NP) on the field of Machine Learning. Would P=NP instantly solve problems like training neural networks with optimal weights or inverse reinforcement learning? Discuss how current deep learning practices rely on heuristics that navigate around these complexity barriers.